What separates an AI pilot from a capability an entire enterprise can rely on?

[PARTNERED]

It is not one model or one successful use case. It is the architecture beneath it: resilient infrastructure, trusted and accessible data, clearly defined access controls, and workflows designed to operate reliably across the organisation.

In Building the AI-Ready Enterprise, Sanjeev Chauhan, Vice President – Cloud Engineering, Oracle India, and Rachit Batham, Senior Vice President, Global Sales and Commercial Strategy – Enterprise AI, Leah, join Leslie D’Monte, Consulting Editor, Mint, to examine what production-ready AI demands from enterprise technology leaders.

The central insight: AI readiness is not merely a technology decision. It begins with the business outcome and depends on infrastructure, data, security and governance working as one foundation.

Don’t have 40 minutes? Go directly to the question most relevant to your organisation:

02:25 | How can CIOs determine whether their infrastructure, data and security architecture are genuinely ready for production AI?
Start with the business mandate, run, grow or change, and then assess whether infrastructure, data availability and security can support it together. Production systems must perform consistently, not merely succeed in a controlled pilot.

10:26 | Which infrastructure decisions must come first when AI moves from pilot to enterprise scale?
Fine-grained security and role-based access must be built into the foundation. AI agents should have defined permissions, just like employees, and should not be able to bypass data-access policies.

12:41 | How should AI-readiness priorities differ between digital-first businesses and enterprises operating legacy systems?
Digital-first companies must govern rapid adoption and avoid an uncontrolled sprawl of agents. Established enterprises need not wait for complete modernisation, but they do need the integration and API infrastructure that allows AI to work with existing systems.

15:54 | What makes enterprise data genuinely AI-ready?
Enterprises do not necessarily need to centralise every dataset. AI can be brought closer to data across on-premise systems, cloud platforms and SaaS applications, creating governed access without requiring constant data movement.

21:41 | Does choosing an LLM, SLM, distilled model or quantised model change the security requirement?
The model should be selected according to the use case, deployment environment and required capabilities. The security expectation remains constant: control what data is shared and what each model or agent can access.

24:20 | How can enterprises move from isolated AI use cases to connected, end-to-end workflows?
The answer is not to bolt AI onto fragmented processes. Enterprises must reimagine the complete workflow, connect functions, govern hand-offs and measure business outcomes—not the number of agents deployed.

31:55 | How can AI transform vendor onboarding, and where must human oversight remain essential?
AI can support document collection, validation, compliance checks and continuous monitoring while escalating exceptions and higher-risk decisions to people. The objective is a process with fewer manual hand-offs—not one without humans.

Watch the full conversation & real world insights from companies on this journey and go directly to the questions that matter to you.

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